Detecting personalities in social media content is an important application of personality psychology. Most early studies apply a coherent piece of writing to personality detection, but today, the challenge is to identify dominant personality traits from a series of short, noisy social media posts. To this end, recent studies have attempted to individually encode the deep semantics of posts, often using attention-based methods, and then relate them, or directly assemble them into graph structures. However, due to the inherently disjointed and noisy nature of social media content, constructing meaningful connections remains challenging. While such methods rely on well-defined relationships be... More >

To address the performance degradation of traditional data association algorithms caused by unknown target motion models, environmental interference, and strong maneuvering behaviors in complex dynamic scenarios, this paper proposes an innovative fusion algorithm that integrates reinforcement learning and deep learning. By constructing a policy network that combines Long Short-Term Memory (LSTM) memory units and reinforcement learning dynamic decision-making, a dynamic prediction model for "measurement-target" association probability is established. Additionally, a hybrid predictor incorporating Bayesian networks and multi-order curve fitting is designed to formulate the reward function. To... More >

Sea ice detection is of vital importance for maritime navigation. Satellite imagery is a crucial medium for conveying information about sea ice. Currently, most sea ice detection models mainly rely on texture information to identify sea ice in satellite imagery, while ignoring sea ice size information. This research presents an improved YOLOv8-Based detection algorithm for multi-scale sea ice. First, we propose a fusion module based on the attention mechanism and use it to replace the Concat module in the YOLOv8 network structure. Second, we conduct an applicability analysis of the bounding box regression loss function in YOLOv8 and ultimately select Shape-IoU, which is more suitable for sea... More >

Few-shot learning aims to recognize new-class items under the circumstances with a few labeled support samples. However, many methods may suffer from poor guidance of limited new-class samples that are not suitable for being regarded as class centers. Recent works use word embedding to enrich the new-class distribution message but only use simple mapping between visual and semantic features during training. To solve the aforementioned problems, we propose a fusion method that constructs a class relation graph by semantic meaning as guidance for feature extraction and fusion, to help the learning of the second-order relation information, with a light training request. In addition, we introduc... More >

In the context of neural network-based radar feature extraction and detection methods, single-feature detection approaches exhibit limited capability in distinguishing targets from background in complex environments such as sea clutter. To address this, a Multi-Feature Extraction Network and Graph Fusion Detection Network (MFEn-GFDn) method is proposed, leveraging feature complementarity and enhanced information utilization. MFEn extracts features from various time-frequency maps of radar signals to construct Multi-Feature Graph Data (MFG) for multi-feature graphical representation. Subsequently, GFDn performs fusion detection on MFG containing multi-feature information. By expanding the fea... More >

With the progressive advancement of remote sensing image technology, its application in the agricultural domain is becoming increasingly prevalent. Both cultivation and transportation processes can greatly benefit from utilizing remote sensing images to ensure adequate food supply. However, such images often exist in harsh environments with many gaps and dense distribution, which poses major challenges to traditional target detection methods. The frequent missed detections and inaccurate bounding boxes severely constrain the further analysis and application of remote sensing images within the agricultural sector. This study presents an enhanced version of the YOLO algorithm, specifically tai... More >

In the evolving framework of the Intelligence of Social Things (IoST), which amalgamates social networks and IoT ecosystems, knowledge graphs are essential for facilitating networked systems to efficiently process and leverage intricate relational data. Knowledge graphs offer essential technical assistance for various artificial intelligence applications, such as e-commerce, intelligent navigation, healthcare, and social media. Nonetheless, current knowledge graphs frequently lack completeness, harboring a considerable quantity of implicit knowledge that remains to be revealed. Consequently, tackling the difficulty of finalising knowledge graphs has emerged as a pressing research priority. M... More >

The methods that identify complex flight maneuvers from multi-sensor flight parameter data fusion and conduct automated quantitative evaluations of anomaly levels could play an important role in enhancing flight safety and pilot training. However, existing methods focus on anomaly detection at individual flight parameter data points, making it challenging to accurately quantify the overall abnormality of a flight maneuver. To address this issue, this paper proposes a novel method for the quantitative evaluation of anomaly levels in complex flight maneuvers using multi-sensor data. The proposed method comprises two stages: complex flight maneuver recognition and anomaly level quantification.... More >